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Estimating Nutrient Concentrations in Wetlands and Biofilters Using Proxy Data Norma Galaviz, Emily Parker, and Cameron Patel August 1, 2013 NSF PIRE Powerpoint Templates

Outline • Background • Introduction • Hypothesis • Methods • Data Analysis • Results • Discussion

Background • The Millennium Drought threatened water security in Melbourne, Australia • Stormwater capture, treatment, and harvesting provide a new source of water for Melbourne, and reduce nitrogen input to Port Phillip Bay

Introduction • Two treatment approaches are evaluated in this study -- biofilters and constructed wetlands • Stormwater treatment is affected by flow path, vegetation, and filter media • The fate of nitrate and phosphate in wetlands and biofilters is evaluated using multiple linear regression (MLR)

Hypothesis Phosphate and nitrate in constructed wetlands and biofilters can be estimated using sensor measurements and other proxy data.

Methods • Sampling took place at a total of six sites in Melbourne, Victoria, Australia. • Two constructed wetlands: Royal Gardens and Hampton Park

Methods • Sampling took place at a total of six sites in Melbourne, Victoria, Australia. • Two constructed wetlands: Royal Gardens and Hampton Park • Two biofilters: Wicks Reserve and Hereford Road

Methods • Sampling took place at a total of six sites in Melbourne, Victoria, Australia. • Two constructed wetlands: Royal Gardens and Hampton Park • Two biofilters: Wicks Reserve and Hereford Road • One bioretention/ constructed wetland system: Lynbrook Estates

Methods • Sampling took place at a total of six sites in Melbourne, Victoria, Australia. • Two constructed wetlands: Royal Gardens and Hampton Park • Two biofilters: Wicks Reserve and Hereford Road • One bioretention/ constructed wetland system: Lynbrook Estates • One natural wetland system: Edithvale-Seaford Wetlands

Methods • Measurements and water samples were collected at multiple locations within each site (including outflow and inflow locations)

Methods • Measurements and water samples were collected at multiple locations within each site (including outflow and inflow locations) • Temperature (T), dissolved oxygen (DO), and pH measurements were recorded ten times per location using portable instruments

Methods • Measurements and water samples were collected at multiple locations within each site (including outflow and inflow locations) • Temperature (T), dissolved oxygen (DO), and pH measurements were recorded ten times per location using portable instruments • Water samples taken in triplicate were analyzed for chlorophyll (CHL), pheophytin (PHEO), nitrate, and phosphate (spectrophotometry), and total suspended solids (TSS; weight measurements) as outlined in Standard Methods for the Examination of Water and Wastewater

Data Analysis MLR models were developed using Virtual Beach 2.3 with: • nitrate or phosphate as the dependent variable • T, DO, pH, CHL, PHEO, and TSS as independent variables

4 groups were evaluated:

Results • MLR models created for each group, ranked by AICc • The R2 values for phosphate models are greater than for nitrate models • Nitrate and phosphate were most highly correlated with TSS and DO

Discussion Phosphate • DO and TSS were the strongest correlates • Consistent with the fact that phosphate binds to suspended solids • Highlights the importance of solids removal as a wetland function

Discussion Nitrate • Nitrate models explained less variance than phosphate models

Discussion Nitrate • Nitrate models explained less variance than phosphate models • DO, TSS, and PHEO were explanatory variables

Discussion Nitrate • Nitrate models explained less variance than phosphate models • DO, TSS, and PHEO were explanatory variables • Nitrate/inverse PHEO relationship suggests nutrient uptake by phytoplankton Phytoplankton Chlorophyll Pheophytin

Discussion Nitrate • Nitrate models explained less variance than phosphate models • DO, TSS, and PHEO were explanatory variables • Nitrate/inverse PHEO relationship suggests nutrient uptake by phytoplankton Phytoplankton Chlorophyll Pheophytin Phytoplankton NO3-

Discussion Nitrate • Nitrate models explained less variance than phosphate models • DO, TSS, and PHEO were explanatory variables • Nitrate/inverse PHEO relationship suggests nutrient uptake by phytoplankton • Note: It is unclear why DO is a correlate. We noticed that DO tended to be low in shallower areas that had higher TSS.

Conclusion • Phosphate can be reasonably estimated using proxy data • Nitrate analysis reveals weak relationship • Further study needed, or perhaps nitrate cannot be reasonably predicted using proxy data.

References Andrieux-Loyer, F.; Aminot, A. Phosphorus forms related to sediment grain size and geochemical characteristics in French coastalA areas. Estuarine, Coastal, Shelf Sci.2001, 52, 617−629. Eaton, A. D.; Franson, M. A. H.; American Public Health Association (Eds.). (2005) Standard Methods for the Examination of Water and Wastewater. (21st ed.) American Public Health Association. Koike, I.; Furuya, K.; Otobe, H.; Nakai, T.; Menoto, T.; Hattori, A. Horizontal distributions of surface chlorophyll a and nitrogenous nutrients near Bering Strait and Unimak Pass. Deep-Sea Res., Part A1982, 29, 149−155. Rippy, M.; Franks, P.; Feddersen, F.; Guza, R.; Warrick, J. Beach Nourishment Impacts on Bacteriological Water Quality and Phytoplankton Bloom Dynamics. Environ. Sci. Technol. 2013, 47, 6146-6154. Rocha, R. R.; Thomaz, S. M.; Carvalho, P.; Gomez, L. C. Modeling chlorophyll a and dissolved oxygen concentration in tropical floodplain lakes (Parana River Brazil). Braz. J. Biol.2009, 69, 491−500.

Acknowledgements This project was funded through a Partnerships for International Research Education grant from the National Science Foundation. Special thanks to the University of Melbourne Engineering Department for lab and equipment accommodations, and to Melbourne Water for site access and cooperation. Thanks also to Dr. Stanley Grant, Dr. Sunny Jiang, Dr. Andrew Mehring, Dr. Meg Rippy, and Alexander McCluskey for their assistance and advice.

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